title: Salience network-based classification and prediction of symptom severity in children with autism.
abstract: OBJECTIVE
  Autism spectrum disorder (ASD) affects 1 in 88 children and is characterized by a complex phenotype, including social, communicative, and sensorimotor deficits. Autism spectrum disorder has been linked with atypical connectivity across multiple brain systems, yet the nature of these differences in young children with the disorder is not well understood.
  
  
  OBJECTIVE
  To examine connectivity of large-scale brain networks and determine whether specific networks can distinguish children with ASD from typically developing (TD) children and predict symptom severity in children with ASD.
  
  
  METHODS
  Case-control study performed at Stanford University School of Medicine of 20 children 7 to 12 years old with ASD and 20 age-, sex-, and IQ-matched TD children.
  
  
  METHODS
  Between-group differences in intrinsic functional connectivity of large-scale brain networks, performance of a classifier built to discriminate children with ASD from TD children based on specific brain networks, and correlations between brain networks and core symptoms of ASD.
  
  
  RESULTS
  We observed stronger functional connectivity within several large-scale brain networks in children with ASD compared with TD children. This hyperconnectivity in ASD encompassed salience, default mode, frontotemporal, motor, and visual networks. This hyperconnectivity result was replicated in an independent cohort obtained from publicly available databases. Using maps of each individual's salience network, children with ASD could be discriminated from TD children with a classification accuracy of 78%, with 75% sensitivity and 80% specificity. The salience network showed the highest classification accuracy among all networks examined, and the blood oxygen-level dependent signal in this network predicted restricted and repetitive behavior scores. The classifier discriminated ASD from TD in the independent sample with 83% accuracy, 67% sensitivity, and 100% specificity.
  
  
  CONCLUSIONS
  Salience network hyperconnectivity may be a distinguishing feature in children with ASD. Quantification of brain network connectivity is a step toward developing biomarkers for objectively identifying children with ASD.
authors.affiliation: Department of Psychiatry, Stanford University School of Medicine, Stanford, California 94305, USA. lucina@stanford.edu
authors.forename: Lucina Q
authors.initials: LQ
authors.lastname: Uddin
authors.forename: Kaustubh
authors.initials: K
authors.lastname: Supekar
authors.forename: Charles J
authors.initials: CJ
authors.lastname: Lynch
authors.forename: Amirah
authors.initials: A
authors.lastname: Khouzam
authors.forename: Jennifer
authors.initials: J
authors.lastname: Phillips
authors.forename: Carl
authors.initials: C
authors.lastname: Feinstein
authors.forename: Srikanth
authors.initials: S
authors.lastname: Ryali
authors.forename: Vinod
authors.initials: V
authors.lastname: Menon
mesh_terms: D001921:Brain / Q000503:physiopathology*; D016022:Case-Control Studies; D002648:Child; D002659:Child Development Disorders, Pervasive / Q000145:classification / Q000175:diagnosis / Q000503:physiopathology*; D015331:Cohort Studies; D063132:Connectome / Q000145:classification / Q000295:instrumentation / Q000379:methods; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods; D008297:Male; D009415:Nerve Net / Q000503:physiopathology*; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index